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Risk Evaluation Model of Coal Spontaneous Combustion Based on AEM-AHP-LSTM

Author

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  • Xu Zhou

    (College of Science, North China University of Science and Technology, Tangshan 063210, China
    School of Mining Engineering, North China University of Science and Technology, Tangshan 063210, China)

  • Shangsheng Ren

    (School of Economics, North China University of Science and Technology, Tangshan 063210, China
    College of Yisheng, North China University of Science and Technology, Tangshan 063210, China)

  • Shuo Zhang

    (School of Basic Medical Sciences, North China University of Science and Technology, Tangshan 063210, China)

  • Jiuling Zhang

    (School of Mining Engineering, North China University of Science and Technology, Tangshan 063210, China)

  • Yibo Wang

    (College of Yisheng, North China University of Science and Technology, Tangshan 063210, China)

Abstract

Immediately and accurately assessing the risk of coal spontaneous combustion and taking targeted action are crucial steps in coal spontaneous combustion prevention and control. A new model, AEM-AHP-LSTM, was proposed to solve the weight calculation problem of multiobjective evaluation in the process of coal spontaneous combustion. Firstly, the key indicators of coal spontaneous combustion were analyzed and used as risk factors to establish an evaluation system. Next, the objective and subjective weights were calculated using AEM and AHP, respectively. The objective and subjective weights were then combined, and TOPSIS was used to calculate the score of the evaluation sample. Finally, the obtained evaluation samples were trained with the BP, RBF, and LSTM model to resolve the problem of model overdependence on historical data and achieve the auto-adapt adjustment of weight with data change. Additionally, data from 15 typical Chinese coal mines were applied to the model. The results indicate that, compared with the BP and RBF neural networks, the LSTM model has higher prediction accuracy, stronger generalization ability, and stronger practicability. The modeling and application findings show that the AEM-AHP-LSTM model was better appropriate for the risk assessment of coal spontaneous combustion. This method can potentially be further applied as reliable approach for the assessment of mine disaster risk.

Suggested Citation

  • Xu Zhou & Shangsheng Ren & Shuo Zhang & Jiuling Zhang & Yibo Wang, 2022. "Risk Evaluation Model of Coal Spontaneous Combustion Based on AEM-AHP-LSTM," Mathematics, MDPI, vol. 10(20), pages 1-16, October.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:20:p:3796-:d:942719
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    References listed on IDEAS

    as
    1. W. Na & Z. C. Zhao, 2021. "The comprehensive evaluation method of low-carbon campus based on analytic hierarchy process and weights of entropy," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(6), pages 9308-9319, June.
    2. Sarraf, Fatemeh & Nejad, Shabnam Hashemi, 2020. "Improving performance evaluation based on balanced scorecard with grey relational analysis and data envelopment analysis approaches: Case study in water and wastewater companies," Evaluation and Program Planning, Elsevier, vol. 79(C).
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